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Modeling Long Cycles

March 30, 2021

Kuala Lumpur Research Seminar Series

  • Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as “long”. In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles, and propose a new econometric procedure for the inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles as characterized by credit and house prices tend to be twice as long as business cycles.

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  • Natasha Kang is a PhD candidate at the Vancouver School of Economics. She completed her BSc and MA at University of British Columbia. Her research interests are in time-series econometrics, macroeconomics and international finance. Her job market paper is concerned with econometric modeling of persistent and low-frequency stochastic cycles - a crucial feature of macroeconomic and financial data.  Her work provides a new econometric framework appropriate for business and financial cycle analysis. She studies the cyclical properties of macroeconomic and financial time series and their implications on macroeconomic models. 


  • WHEN (KUALA LUMPUR TIME): Tuesday, March 30, 2021 - 8:30 -9:30am
  • WHEN (ET/WASHINGTON, D.C. TIME): Monday, March 29, 2021 - 8:30 – 9:30pm